Using Auto-Videosomnography to Study the Relation between Sleep and Nightwaking in Infancy

The aim of this study was to test whether auto-videosomnography, which has been shown to be as accurate as actigraphy for documenting infants’ sleep characteristics (Horger, et al., 2021), could efficiently capture the relation between infants’ sleep and motor development.

Sarah Berger, Natalie Barnett, Shambhavi Thakur

Presented at World Sleep Congress, Rome, 2022

Abstract

Introduction 

The onset of motor milestones, such as crawling and walking disrupts infants’ sleep. For example, as measured via actigraphy, crawling infants woke more frequently during the night than age matched controls who had not yet begun to crawl (Scher, 2005). Intensive longitudinal analysis of prospective parent diaries showed that the first day infants performed a motor skill, as well as the day they demonstrated mastery, predicted increased night wakings and shorter sleep durations (Berger & Moore, 2021). These traditional methods of collecting sleep and motor data are too intrusive, costly, and/or effortful to feasibly collect data on a large scale. However, because infant sleep is so variable from night-to-night, large-scale data collection is necessary to describe periods of stable sleep and identify deviations from stability. Thus, the aim of this study was to test whether auto-videosomnography, which has been shown to be as accurate as actigraphy for documenting infants’ sleep characteristics (Horger, et al., 2021), could efficiently capture the relation between infants’ sleep and motor development. 

Materials and Methods 

1302 parents of infants between the ages of 9 and 14 months completed the Survey of Well being of Young Children (SWYC) on-line. The SWYC is a 40-question screening instrument for children under 5 years old that includes cognitive, language, motor and social-emotional subscales. All participants were users of Nanit, a commercial, home video baby monitoring system that video-records infants in their cribs. A sophisticated machine learning algorithm uses computer vision technology to calculate and report sleep characteristics including nightly wake episodes. For the purposes of this abstract, only data from the motor subscale and from families who reported their infant’s walking experience (not yet, somewhat, very much; n=279) are reported. 

Results 

A 6 (age; 9, 10, 11, 12, 13, 14 mos) x 3 (experience) ANOVA on the number of night wakings revealed a significant main effect of walk experience, F(5, 261)=4.05, p=.02. Across ages, but especially for younger infants, those whose parents reported that they walk very much woke more frequently than those with less experience.

Conclusions 

These findings replicate previous work on the onset of crawling and pulling-to-stand showing that infants’ sleep was most disrupted for those who achieved their motor milestones earlier than average (Atun-Einy & Scher, 2016; Scher & Cohen, 2015) and extends it to the new milestone context of walking. This study demonstrates the feasibility of using auto-videosomnography to study the relation between sleep and motor development in infancy. Thus, this new method has the potential to collect data more efficiently and on a larger scale than has been done to date. This creates opportunities for researchers to track individual developmental trajectories and, in turn, the power to predict change rather than just document it. 

Using Auto-Videosomnography to Study the Relation between Sleep and Nightwaking in Infancy

About the researchers

The authors include Sarah Berger, Natalie Barnett, and Shambhavi Thakur

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  • Dr. Sarah Berger is a Professor of Psychology at the College of Staten Island and the Graduate Center of the City University of New York. She received her PhD from New York University. Dr. Berger was an American Association of University Women Postdoctoral Research Fellow and a Fulbright Research Scholar. Dr. Berger studies the interaction between cognitive and motor development in infancy, particularly response inhibition and its implications for the allocation of attention in very young children. A line of National Science Foundation (NSF)-funded work, in collaboration with Dr. Anat Scher, has been the first to study the impact of sleep on motor problem solving in infancy.

  • Shambhavi Thakur serves as Clinical Research Data Analyst at Nanit. She holds a Masters degree in Health Informatics and Life Sciences. She oversees the research collaborations with various universities and analyzes sleep data for internal as well as external studies.

  • Dr. Natalie Barnett serves as VP of Clinical Research at Nanit. Natalie initiated sleep research collaborations at Nanit and in her current role, Natalie oversees collaborations with researchers at hospitals and universities around the world who use the Nanit camera to better understand pediatric sleep and leads the internal sleep and development research programs at Nanit. Natalie holds a Ph.D. in Genetics from the University of New England in Australia and a Postgraduate Certificate in Pediatric Sleep Science from the University of Western Australia. Natalie was an Assistant Professor in the Neurogenetics Unit at NYU School of Medicine prior to joining Nanit. Natalie is also the voice of Nanit's science-backed, personalized sleep tips delivered to users throughout their baby's first few years.

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